SoroushVahidi/scidocs huggingface dataset:
Ternary Residual Networks
(2017)

Sub-8-bit representation of DNNs incur some noticeable loss of accuracy despite rigorous (re)training at low-precision. Such loss of accuracy essentially makes them equivalent to a much shallower counterpart, diminishing the power of being deep networks. To address this problem of accuracy drop we introduce the notion of residual networks where we add more low-precision edges to sensitive branches of the sub-8-bit network to compensate for the lost accuracy. Further, we present a perturbation theory to identify such sensitive edges. Aided by such an elegant trade-off between accuracy and model size, the 8-2 architecture (8-bit activations, ternary weights), enhanced by residual ternary edges, turns out to be sophisticated enough to achieve similar accuracy as 8-8 representation (∼ 1% drop from our FP-32 baseline), despite ∼ 1.6× reduction in model size, ∼ 26× reduction in number of multiplications , and potentially ∼ 2× inference speed up comparing to 8-8 representation, on the state-of-the-art deep network ResNet-101 pre-trained on ImageNet dataset. Moreover, depending on the varying accuracy requirements in a dynamic environment, the deployed low-precision model can be upgraded/downgraded on-the-fly by partially enabling/disabling residual connections. For example, disabling the least important residual connections in the above enhanced network, the accuracy drop is ∼ 2% (from our FP-32 baseline), despite ∼ 1.9× reduction in model size, ∼ 32× reduction in number of multiplications, and potentially ∼ 2.3× inference speed up comparing to 8-8 representation. Finally, all the ternary connections are sparse in nature, and the residual ternary conversion can be done in a resource-constraint setting without any low-precision (re)training and without accessing the data.
A Compact Meander-Line UHF RFID Tag Antenna Loaded With Elements Found in Right/Left-Handed Coplanar Waveguide Structures
(2009)

A new planar meander-line antenna for passive UHF radio frequency identification (RFID) tags is presented. Specifically, a meander-line antenna is loaded periodically with coplanar waveguide (CPW) LC elements traditionally found in right/left-handed waveguide structures. It is shown that by using the antenna presented in this letter in a prototype passive UHF RFID tag, effective read ranges up to 4.87 m can be achieved. Many different dielectric substrates and CPW-LC load dimensions are investigated to illustrate how the input impedance, gain, and overall dimensions of the antenna are affected by these structural differences. It is shown that the overall dimension of the meander-line antenna can be reduced by slightly more than 18% with the introduction of the CPW-LC elements to the design. Several of the simulation results are validated by comparison with measurements.
An Attribute-Based High-Level Image Representation for Scene Classification
(2019)

Scene classification is increasingly popular due to its extensive usage in many real-world applications such as object detection, image retrieval, and so on. Traditionally, the low-level hand-crafted image representations are adopted to describe the scene images. However, they usually fail to detect semantic features of visual concepts, especially in handling complex scenes. In this paper, we propose a novel high-level image representation which utilizes image attributes as features for scene classification. More specifically, the attributes of each image are firstly extracted by a deep convolution neural network (CNN), which is trained to be a multi-label classifier by minimizing an element-wise logistic loss function. The process of generating attributes can reduce the “semantic gap” between the low-level feature representation and the high level scene meaning. Based on the attributes, we then build a system to discover semantically meaningful descriptions of the scene classes. Extensive experiments on four large-scale scene classification datasets show that our proposed algorithm considerably outperforms other state-of-the-art methods.
Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
(2018)

Employing part-level features offers fine-grained information for pedestrian image description.A prerequisite of part discovery is that each part should be well located. Instead of using external resources like pose estimator, we consider content consistency within each part for precise part location. Specifically, we target at learning discriminative part-informed features for person retrieval and make two contributions. (i) A network named Part-based Convolutional Baseline (PCB). Given an image input, it outputs a convolutional descriptor consisting of several part-level features. With a uniform partition strategy, PCB achieves competitive results with the state-of-the-art methods, proving itself as a strong convolutional baseline for person retrieval. (ii) A refined part pooling (RPP) method. Uniform partition inevitably incurs outliers in each part, which are in fact more similar to other parts. RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency. Experiment confirms that RPP allows PCB to gain another round of performance boost. For instance, on the Market-1501 dataset, we achieve (77.4+4.2)% mAP and (92.3+1.5)% rank-1 accuracy, surpassing the state of the art by a large margin. Code is available at: https://github.com/syfafterzy/PCB_RPP
Can Rumour Stance Alone Predict Veracity?
(2018)

Prior manual studies of rumours suggested that crowd stance can give insights into the actual rumour veracity. Even though numerous studies of automatic veracity classification of social media rumours have been carried out, none explored the effectiveness of leveraging crowd stance to determine veracity. We use stance as an additional feature to those commonly used in earlier studies. We also model the veracity of a rumour using variants of Hidden Markov Models (HMM) and the collective stance information. This paper demonstrates that HMMs that use stance and tweets’ times as the only features for modelling true and false rumours achieve F1 scores in the range of 80%, outperforming those approaches where stance is used jointly with content and user based features.
Investigation on perceptual and robustness of LSB digital watermarking scheme on Halal Logo authentication
(2012)

In Malaysia, Halal Certificate can only be issued by Jakim. For the time being, there is no specific method to verify authentication of Halal Certificate displayed at food or service premises. Watermarking technique however, offered a solution for authenticity of the data and copyright protection. In this paper, an investigation on perceptual and robustness of least significant bit (LSB) digital watermarking scheme on Halal Logo authentication is implemented using MATLAB software. LSB digital watermarking scheme allows pixel value modification by dividing its entire bit leaving most significant bit (contains most information) and least significant bit (contains less information). These small modifications offer a high perceptual transparency to the watermarked image. A Quick response (QR) code with message is generated as an embedded watermark image for Halal Logo. The watermarked image quality is measured based on Peak signal-to-noise (PSNR), mean square error (MSE), and Normalized Cross-Correlation (NC). The investigation shows the scheme provided high PSNR performance which is between 12-22 dB with Gaussian noise added. The scheme successfully shows the ability to retrieve the embed watermark even though the cover image visual quality is degraded with 50 % Gaussian noise variance. The reliability of the scheme is proven when it successfully to produce an acceptable 0.8442 NC value. The observation on perceptibility shows 51 dB of PSNR with 0.4714 MSE.
FastQRE: Fast Query Reverse Engineering
(2018)

We study the problem of Query Reverse Engineering (QRE), where given a database and an output table, the task is to find a simple project-join SQL query that generates that table when applied on the database. This problem is known for its efficiency challenge due to mainly two reasons. First, the problem has a very large search space and its various variants are known to be NP-hard. Second, executing even a single candidate SQL query can be very computationally expensive. In this work we propose a novel approach for solving the QRE problem efficiently. Our solution outperforms the existing state of the art by 2-3 orders of magnitude for complex queries, resolving those queries in seconds rather than days, thus making our approach more practical in real-life settings.
Putting Things in Context: Community-specific Embedding Projections for Sentiment Analysis
(2015)

Variation in language is ubiquitous, and is particularly evident in newer forms of writing such as social media. Fortunately, variation is not random, but is usually linked to social factors. By exploiting linguistic homophily — the tendency of socially linked individuals to use language similarly — it is possible to build models that are more robust to variation. In this paper, we focus on social network communities, which make it possible to generalize sociolinguistic properties from authors in the training set to authors in the test sets, without requiring demographic author metadata. We detect communities via standard graph clustering algorithms, and then exploit these communities by learning community-specific projections of word embeddings. These projections capture shifts in word meaning in different social groups; by modeling them, we are able to improve the overall accuracy of Twitter sentiment analysis by a significant margin over competitive prior work.
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal
(2018)

Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data. The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of travel, through buildings and outdoor regions that collectively exhibit large variations in visual appearance, self-similarity, and connectivity. We compare pretrained visual encoders that enable precomputation of visual embeddings to achieve a throughput of tens of thousands of transitions per second at training time on a commodity desktop computer, allowing agents to learn from millions of trajectories of experience in a matter of hours. We propose multiple forms of computationally efficient stochastic augmentation to enable the learned policy to generalise beyond these precomputed embeddings, and demonstrate successful deployment of the learned policy on the real robot without fine tuning, despite environmental appearance differences at test time. The dataset and code required to reproduce these results and apply the technique to other datasets and robots is made publicly available at rl-navigation.github.io/deployable.
Virtual reality using games for improving physical functioning in older adults: a systematic review
(2014)

The use of virtual reality through exergames or active video game, i.e. a new form of interactive gaming, as a complementary tool in rehabilitation has been a frequent focus in research and clinical practice in the last few years. However, evidence of their effectiveness is scarce in the older population. This review aim to provide a summary of the effects of exergames in improving physical functioning in older adults. A search for randomized controlled trials was performed in the databases EMBASE, MEDLINE, PsyInfo, Cochrane data base, PEDro and ISI Web of Knowledge. Results from the included studies were analyzed through a critical review and methodological quality by the PEDro scale. Thirteen studies were included in the review. The most common apparatus for exergames intervention was the Nintendo Wii gaming console (8 studies), followed by computers games, Dance video game with pad (two studies each) and only one study with the Balance Rehabilitation Unit. The Timed Up and Go was the most frequently used instrument to assess physical functioning (7 studies). According to the PEDro scale, most of the studies presented methodological problems, with a high proportion of scores below 5 points (8 studies). The exergames protocols and their duration varied widely, and the benefits for physical function in older people remain inconclusive. However, a consensus between studies is the positive motivational aspect that the use of exergames provides. Further studies are needed in order to achieve better methodological quality, external validity and provide stronger scientific evidence.
A Novel Implementation of Phase Control Technique for Speed Control of Induction Motor Using ARDUINO
(2013)

Induction motors are the most widely used motors for appliances, industrial control and automation. However there arises a problem in voltage levels, which affects the speed of induction motor. As the voltage (V) is directly proportional to speed (N), we need to control the stator voltage which controls the speed proportionally. In this Paper a novel Open loop phase control method is developed by coding a program using ARDUINO software in which ARDUINO controller takes input from the user and generates firing pulses for the TRIAC which controls the speed of the Induction motor. The total process is executed with the help of an ARDUINO controller kit where ARDUINO and Tera-Term softwares are used for Micro Controller and for serial monitor. This results in variable speed control of Induction motor.
The Gremlin graph traversal machine and language (invited talk)
(2015)

Gremlin is a graph traversal machine and language designed, developed, and distributed by the Apache TinkerPop project. Gremlin, as a graph traversal machine, is composed of three interacting components: a graph, a traversal, and a set of traversers. The traversers move about the graph according to the instructions specified in the traversal, where the result of the computation is the ultimate locations of all halted traversers. A Gremlin machine can be executed over any supporting graph computing system such as an OLTP graph database and/or an OLAP graph processor. Gremlin, as a graph traversal language, is a functional language implemented in the user's native programming language and is used to define the traversal of a Gremlin machine. This article provides a mathematical description of Gremlin and details its automaton and functional properties. These properties enable Gremlin to naturally support imperative and declarative querying, host language agnosticism, user-defined domain specific languages, an extensible compiler/optimizer, single- and multi-machine execution models, hybrid depth- and breadth-first evaluation, as well as the existence of a Universal Gremlin Machine and its respective entailments.
Understanding Internet Banking Adoption and Use Behavior: A Hong Kong Perspective
(2004)

Hong Kong is an international financial center well known for its efficiency and its ability to adapt and keep up with the times. Recently, however, the Hong Kong banking industry has been losing competitive advantages in some areas, with the adoption of Internet Banking being one of them. Hong Kong banks have been slower than some other international banks in joining the e-commerce evolution, which first emerged in the United States in mid-90s. Financial institutions in the U.S. have introduced and promoted online banking to provide better customer services. Many property and stock investment firms in Hong Kong have also jumped on the bandwagon and adopted the Internet as a channel for providing better and more efficient services ABSTRACT
Phone Merging For Code-Switched Speech Recognition
(2018)

Speakers in multilingual communities often switch between or mix multiple languages in the same conversation. Automatic Speech Recognition (ASR) of codeswitched speech faces many challenges including the influence of phones of different languages on each other. This paper shows evidence that phone sharing between languages improves the Acoustic Model performance for Hindi-English code-switched speech. We compare baseline system built with separate phones for Hindi and English with systems where the phones were manually merged based on linguistic knowledge. Encouraged by the improved ASR performance after manually merging the phones, we further investigate multiple data-driven methods to identify phones to be merged across the languages. We show detailed analysis of automatic phone merging in this language pair and the impact it has on individual phone accuracies and WER. Though the best performance gain of 1.2% WER was observed with manually merged phones, we show experimentally that the manual phone merge is not optimal.
Constant Communication ORAM with Small Blocksize
(2015)

There have been several attempts recently at using homomorphic encryption to increase the efficiency of Oblivious RAM protocols. One of the most successful has been Onion ORAM, which achieves O(1) communication overhead with polylogarithmic server computation. However, it has two drawbacks. It requires a large block size of B = Ω(log6 N) with large constants. Moreover, while it only needs polylogarithmic computation complexity, that computation consists mostly of expensive homomorphic multiplications. In this work, we address these problems and reduce the required block size to Ω(log4 N). We remove most of the homomorphic multiplications while maintaining O(1) communication complexity. Our idea is to replace their homomorphic eviction routine with a new, much cheaper permute-and-merge eviction which eliminates homomorphic multiplications and maintains the same level of security. In turn, this removes the need for layered encryption that Onion ORAM relies on and reduces both the minimum block size and server computation.
Monocular Pedestrian Detection: Survey and Experiments
(2009)

Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade, HOG/linSVM, NN/LRF, and combined shape-texture detection. Experiments are performed on an extensive data set captured onboard a vehicle driving through urban environment. The data set includes many thousands of training samples as well as a 27-minute test sequence involving more than 20,000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The data set (8.5 GB) is made public for benchmarking purposes.
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
(2006)

We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using AdaBoost for feature selection, we identify the appropriate set of blocks, from a large set of possible blocks. In our system, we use the integral image representation and a rejection cascade which significantly speed up the computation. For a 320 × 280 image, the system can process 5 to 30 frames per second depending on the density in which we scan the image, while maintaining an accuracy level similar to existing methods.
Flexible HALS algorithms for sparse non-negative matrix/tensor factorization
(2008)

In this paper we propose a family of new algorithms for non-negative matrix/tensor factorization (NMF/NTF) and sparse nonnegative coding and representation that has many potential applications in computational neuroscience, multi-sensory, multidimensional data analysis and text mining. We have developed a class of local algorithms which are extensions of hierarchical alternating least squares (HALS) algorithms proposed by us in . For these purposes, we have performed simultaneous constrained minimization of a set of robust cost functions called alpha and beta divergences. Our algorithms are locally stable and work well for the NMF blind source separation (BSS) not only for the over-determined case but also for an under-determined (over-complete) case (i.e., for a system which has less sensors than sources) if data are sufficiently sparse. The NMF learning rules are extended and generalized for N-th order nonnegative tensor factorization (NTF). Moreover, new algorithms can be potentially accommodated to different noise statistics by just adjusting a single parameter. Extensive experimental results confirm the validity and high performance of the developed algorithms, especially, with usage of the multi-layer hierarchical approach .
We don't need another hero.
(2001)

Everybody loves the stories of heroes like Martin Luther King, Jr., Mother Teresa, and Gandhi. But the heroic model of moral leadership usually doesn't work in the corporate world. Modesty and restraint are largely responsible for the achievements of the most effective moral leaders in business. The author, a specialist in business ethics, says the quiet leaders he has studied follow four basic rules in meeting ethical challenges and making decisions. The rules constitute an important resource for executives who want to encourage the development of such leaders among their middle managers. The first rule is "Put things off till tomorrow." The passage of time allows turbulent waters to calm and lets leaders' moral instincts emerge. "Pick your battles" means that quiet leaders don't waste political capital on fights they can't win; they save it for occasions when they really want to fight. "Bend the rules, don't break them" sounds easier than it is--bending the rules in order to resolve a complicated situation requires imagination, discipline, restraint, flexibility, and entrepreneurship. The fourth rule, "Find a compromise," reflects the author's finding that quiet leaders try not to see situations as polarized tests of ethical principles. These individuals work hard to craft compromises that are "good enough"--responsible and workable enough--to satisfy themselves, their companies, and their customers. The vast majority of difficult problems are solved through the consistent striving of people working far from the limelight. Their quiet approach to leadership doesn't inspire, thrill, or provide story lines for uplifting TV shows. But the unglamorous efforts of quiet leaders make a tremendous difference every day in the corporate world.